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Record W4247622457 · doi:10.35940/ijrte.c5389.118419

Preliminary Assessment on the Effect of Bentonite and Ibeshe Clay on Bleaching of Rubber (Hevea Brasiliensis) Seed Oil

2019· article· en· W4247622457 on OpenAlexfundno aff
O.R. Obanla, Joseph Denis Udonne, Olayinka O. Ajani, Augustine O. Ayeni, Farouk U. Mohammed

Bibliographic record

VenueInternational Journal of Recent Technology and Engineering (IJRTE) · 2019
Typearticle
Languageen
FieldNursing
TopicPomegranate: compositions and health benefits
Canadian institutionsnot available
FundersCovenant UniversityEuropean CommissionInternational Development Research Centre
KeywordsBentoniteHevea brasiliensisNatural rubberHydrochloric acidHeveaYield (engineering)Pulp and paper industryChemistryNuclear chemistryMaterials scienceChemical engineeringComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

Rubber seed though not common known is very rich in its oil yield and over time its importance is becoming more prominent. Bleaching in the natural sense is relative to the removal of impurities from the oil or material. In this research work, Rubber seed oil was bleached using bentonite clay and Ibeshe clay at 0.5M, 1M and 2M concentrations of Hydrochloric acid. Physiochemical properties as well as spectroscopic analysis such as FT-IR and XRD analysis were carried out and aided in obtaining the bleaching efficiency of both clays. The FT-IR results displayed a visible change in the oil after it was bleached with Ibeshe clay but still retained most its functional group when bleached with bentonite clay. At 2M concentration of the acid, the oil bleached with bentonite showed 53% while with Ibeshe clay it remained at 16%. This summarizes that Ibeshe clay has little to no effect on bleaching performance.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.342
Threshold uncertainty score0.357

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.005
GPT teacher head0.264
Teacher spread0.258 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

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